This section covers the idea of selecting columns from your data. Returning a subset of columns that are relevant to your analysis, making it easier to work with and understand the dataset. This process helps in condensing or minimizing the dataset, reducing its column size to focus on the essential information.
Python - selecting
Code
from datavolley import read_dvimport pandas as pdfrom IPython.display import Markdowndv_instance = read_dv.DataVolley(None) # Replace `None` with path of your dvw file df = dv_instance.get_plays()selected_df = df[['team', 'player_name', 'skill', 'evaluation_code', 'point_won_by']].head(10)Markdown(selected_df.to_markdown(index =False))
team
player_name
skill
evaluation_code
point_won_by
University of Louisville
nan
nan
nan
nan
University of Louisville
nan
nan
nan
nan
University of Dayton
nan
nan
nan
nan
University of Dayton
nan
nan
nan
nan
University of Louisville
Shannon Shields
Serve
+
University of Louisville
University of Dayton
Maura Collins
Reception
-
University of Louisville
University of Dayton
Brooke Westbeld
Set
#
University of Louisville
University of Dayton
Jamie Peterson
Attack
-
University of Louisville
University of Louisville
Anna Stevenson
Block
+
University of Louisville
University of Louisville
Shannon Shields
Dig
+
University of Louisville
R - selecting
Code
library(datavolley)library(dplyr)x<-dv_read("datavolley//example_data.dvw")# Example data from pythonpx<-x$playsselected_px<-px%>%dplyr::select(team, player_name, skill, evaluation_code, point_won_by)knitr::kable(selected_px%>%head(10))